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Background: The KwaZulu-Natal Sandstone Sourveld (KZN SS) is a grassland ecosystem categorised as endangered by the terms of the National Environmental Management: Biodiversity Act (Act 10 of 2004). Pressure from urbanisation has led to the remaining areas of the KZN SS being physically fragmented, causing low connectivity levels which have diminished the biological persistence of this ecosystem. Objectives: This study aimed to quantify the overall level of habitat fragmentation of the KZN SS and determine the level of connectivity within the ecosystem, and patches of the KZN SS occurring in the eThekwini Municipal area. Using graph theory, we compared the effectiveness of broad-scale and fine-scale data sets in quantifying habitat fragmentation. Methods: The Conefor Sensinode software, which employs the bases of graph theory, was chosen to aid in assessing fragmentation levels. The integral index of connectivity (IIC) (values range from 0 to 1, with 1 highlighting optimal connectivity) was chosen as the best index to determine landscape connectivity. Results: The KZN SS was shown to be highly fragmented, especially at dispersal distances less than 500 m. This resulted in very low connectivity levels, with the highest IIC value recorded at 0.0063. Moreover, the use of either fine-scale or broad-scale data to measure connectivity differed substantially at 1000 m. Conclusion: Broad-scale data were shown to overestimate habitat fragmentation and underestimate landscape connectivity. A more apt description of KZN SS connectivity levels was realised with fine-scale data. This study recommends that crucial KZN SS patches be prioritised to safeguard this endangered ecosystem. [ABSTRACT FROM AUTHOR]

• The majority of KwaZulu-Natal's SOC stocks were found to be distributed between 60–200 cm. • Forested areas in KwaZulu-Natal had the greatest SOC concentration per unit Area. • Rainfall, clay content, temperature, elevation, and RVI were found to be key determinants of SOC prediction at different depths. • DNN model performed well in topsoil (0–30 cm), but less accurate with deeper depth. The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model's prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change. [ABSTRACT FROM AUTHOR]